Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology

• Published in: PloS one 2023-03, Vol.18 (3), p.e0278084-e0278084
• Main Authors: Duenweg, Savannah R, Brehler, Michael, Bobholz, Samuel A, Lowman, Allison K, Winiarz, Aleksandra, Kyereme, Fitzgerald, Nencka, Andrew, Iczkowski, Kenneth A, LaViolette, Peter S
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Annotations; Artificial intelligence; Automation; Biology and Life Sciences; Cancer therapies; Comparative analysis; Computer and Information Sciences; Computer-aided medical diagnosis; Datasets; Deep Learning; Diagnosis; Digitization; Histology; Humans; Learning algorithms; Machine Learning; Male; Medical diagnosis; Medical prognosis; Medical research; Medical screening; Medicine and Health Sciences; Methods; Microscopy, Medical; Modelling; Neoplasm Grading; Neural networks; Pathology; Patients; Prognosis; Prostate - pathology; Prostate cancer; Prostatic Neoplasms - pathology; Regression analysis; Technology application; Tiles; Tumors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0278084

One in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to inter-reviewer variability. This study compares two machine learning methods for discriminating between cancerous regions on digitized histology from 47 PCa patients. Whole-slide images were annotated by a GU fellowship-trained pathologist for each Gleason pattern. High-resolution tiles were extracted from annotated and unlabeled tissue. Patients were separated into a training set of 31 patients (Cohort A, n = 9345 tiles) and a testing cohort of 16 patients (Cohort B, n = 4375 tiles). Tiles from Cohort A were used to train a ResNet model, and glands from these tiles were segmented to calculate pathomic features to train a bagged ensemble model to discriminate tumors as (1) cancer and noncancer, (2) high- and low-grade cancer from noncancer, and (3) all Gleason patterns. The outputs of these models were compared to ground-truth pathologist annotations. The ensemble and ResNet models had overall accuracies of 89% and 88%, respectively, at predicting cancer from noncancer. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. Our results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; however, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.